An ideal denture base must have good physical and mechanical properties, biocompatibility, and esthetic properties. Various polymeric materials have been used to construct denture bases. Polymethyl methacrylate (PMMA) is the most used biomaterial for dentures fabrication due to its favorable properties, which include ease of processing and pigmenting, sufficient mechanical properties, economy, and low toxicity. This article aimed to comprehensively review the current knowledge about denture base materials (DBMs) types, properties, modifications, applications, and construction methods. We searched for articles about denture base materials in PubMed, Scopus, and Embase. Journals covering topics including dental materials, prosthodontics, and restorative dentistry were also combed through. Denture base material variations, types, qualities, applications, and fabrication research published in English were considered. Although PMMA has several benefits and gained popularity as a denture base material, it has certain limitations and cannot be classified as an ideal biomaterial for fabricating dental prostheses. Accordingly, several studies have been performed to enhance the physical and mechanical properties of PMMA by chemical modifications and mechanical reinforcement using fibers, nanofillers, and hybrid materials. This review aimed to update the current knowledge about DBMs’ types, properties, applications, and recent developments. There is a need for specific research to improve their biological properties due to patient and dental staff adverse reactions to possibly harmful substances produced during their manufacturing and use.

The aim of this article is to comprehensively review the revolution of dental zirconia (Zir), including its types, properties, applications, and cementation procedures. A comprehensive search of PubMed and Embase was conducted. The search was limited to manuscripts published in English. The final search was conducted in October 2021. Newly developed monolithic Zir ceramics have substantially enhanced esthetics and translucency. However, this material must be further studied in vitro and in vivo to determine its long-term ability to maintain its exceptional properties. According to the literature, monolithic translucent Zir has had promising results and a high survival rate. Thus, the utilization of this material is indicated when strength and esthetics are needed. Both the materials and methods used for cementation of monolithic Zir have significantly improved, encouraging dentists to use this material, especially when a conservative approach is required. Zir restorations showed promising outcomes, particularly for monolithic Zir crowns supported with implant and fixed dental prostheses.

This systematic review aimed to compare the clinical outcome, internal gap, trueness, precision, and biocompatibility of 3D-printed (AM) compared to milled (SM) zirconia restorations. A thorough search of Internet databases was conducted up to September 2023. The search retrieved studies compared AM zirconia to SM zirconia restorations regarding clinical outcome, fit, trueness, precision, and biocompatibility. Of 1736 records, only 59 were screened for eligibility, and 22 records were included in this review. The quality of studies was assessed using the revised Cochrane risk-of-bias tool (ROB2), and the Modified Consort Statement. One clinical study exhibited a low risk of bias. All laboratory studies revealed some bias concerns. Short-term observation showed 100 % survival with no signs of periodontal complications. 3D-printed zirconia crowns showed statistically significant lower ΔE and a better match to adjacent teeth (p ≤ 0.5). The fit, trueness, and precision vary with the printing technique and the tooth surface. 3D-printed zirconia crowns provide better aesthetic color and contour match to adjacent natural teeth than milled crowns. Both 3D printing and milling result in crowns within the clinically acceptable internal and marginal fit. Except for nanoparticle jetting, the marginal gap of SM crowns was smaller than AM crowns, however, both were clinically acceptable. Laminate veneers might be more accurately produced by 3D printing. 3D-printed axial surface trueness was better than milled axial surfaces. Long-term RCTs are recommended to confirm the clinical applicability of 3D-printed restorations. Internal fit and gap, precision, and trueness are fundamental requirements for successful dental restorations. Both techniques produce restorations with clinically acceptable marginal and internal fit. Axial surfaces and narrow or constricted areas favored 3D-printed than conventionally milled zirconia.

The rapid development of artificial intelligence (AI) has led to the emergence of many new technologies in the healthcare industry. In dentistry, the patient’s panoramic radiographic or cone beam computed tomography (CBCT) images are used for implant placement planning to find the correct implant position and eliminate surgical risks. This study aims to develop a deep learning-based model that detects missing teeth’s position on a dataset segmented from CBCT images. Five hundred CBCT images were included in this study. After preprocessing, the datasets were randomized and divided into 70% training, 20% validation, and 10% test data. A total of six pretrained convolutional neural network (CNN) models were used in this study, which includes AlexNet, VGG16, VGG19, ResNet50, DenseNet169, and MobileNetV3. In addition, the proposed models were tested with/without applying the segmentation technique. Regarding the normal teeth class, the performance of the proposed pretrained DL models in terms of precision was above 0.90. Moreover, the experimental results showed the superiority of DenseNet169 with a precision of 0.98. In addition, other models such as MobileNetV3, VGG19, ResNet50, VGG16, and AlexNet obtained a precision of 0.95, 0.94, 0.94, 0.93, and 0.92, respectively. The DenseNet169 model performed well at the different stages of CBCT-based detection and classification with a segmentation accuracy of 93.3% and classification of missing tooth regions with an accuracy of 89%. As a result, the use of this model may represent a promising time-saving tool serving dental implantologists with a significant step toward automated dental implant planning.